Using computational modelling to understand the effects of pharmacological manipulations on cognitive function

使用计算模型来了解药理操作对认知功能的影响

• 批准号: 2081400
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2081400
• 关键词: Using; computational; modelling; understand; effects

This collaborative project is a partnership between Prof Jonathan Roiser (UCL Institute of Cognitive Neuroscience) and Cambridge Cognition Ltd. Prof Roiser's group focuses on understanding the cognitive and neural mechanisms underpinning mental health problems, especially depression. Over the past 5 years his work increasingly adopted a computational approach, using generative models to better understand patterns of behaviour (and brain responses) observed when human participants perform cognitive tasks.Cambridge Cognition is a world leader in developing computerised cognitive testing, and it sells the well-known Cambridge Neuropsychological Test Automated Battery (CANTAB) software. It develops, often in partnership with academics, computerised tests that allow the measurement of specific domains of cognition, for example executive function, memory, attention and decision making. The tests it develops are based on a wealth of neuroscientific data from humans and animals.The analysis of data derived from computerised cognitive tests is an active area of development, and over the past decade it has become increasingly common to use a computational approach to create parameters that summarise specific cognitive processes. This approach involves specifying, in mathematical form, precisely how the experimenter believes that the participant is completing the task. This is known as a "generative model" (because, given the same task as the human would perform, the model can generate behavioural responses). The behaviour of the model is governed by specific parameters, which can be interpreted in a cognitive framework, and by fitting the model to data collected in human participants it is possible to estimate parameters. These parameters then serve as summary statistics, in contrast to the traditional descriptive approach to data analysis, which usually involves simply calculating means or differences between conditions. The major advantage of the computational approach is that it can capitalise on the richness inherent in the data, which is usually overlooked in the traditional approach (for example, processes that evolve gradually over time which are difficult to capture using a purely descriptive approach).A major aim of this project is to develop computational models for CANTAB tests, which are currently analysed using traditional descriptive approaches. Cambridge Cognition owns a wealth of data collected in the general population (both face-to-face and using its online platform) that can be used for this purpose. Following the development of the models, experiments will be conducted to understand how the model parameters are influenced by symptoms of mental health problems (especially depression), and by pharmacological interventions, particularly with cognitive enhancing drugs.

这个合作项目是Jonathan Roiser教授（伦敦大学学院认知神经科学研究所）和剑桥认知有限公司之间的合作伙伴关系。Roiser教授的团队专注于了解心理健康问题，特别是抑郁症的认知和神经机制。在过去的5年里，他的工作越来越多地采用计算方法，使用生成模型来更好地理解人类参与者执行认知任务时观察到的行为模式（和大脑反应）。剑桥认知是开发计算机化认知测试的世界领导者，它销售著名的剑桥神经心理测试自动电池（CANTAB）软件。它通常与学者合作开发计算机化测试，可以测量特定的认知领域，例如执行功能、记忆、注意力和决策。它开发的测试基于来自人类和动物的大量神经科学数据。对来自计算机认知测试的数据进行分析是一个活跃的发展领域，在过去的十年中，使用计算方法来创建总结特定认知过程的参数变得越来越普遍。这种方法需要以数学形式精确地说明实验者认为参与者是如何完成任务的。这被称为“生成模型”（因为，给定与人类执行相同的任务，该模型可以生成行为反应）。该模型的行为由特定参数控制，这些参数可以在认知框架中进行解释，并且通过将该模型拟合到在人类参与者中收集的数据，可以估计参数。这些参数然后作为汇总统计，与传统的描述性数据分析方法，通常涉及简单地计算平均值或条件之间的差异。计算方法的主要优点是它可以利用数据中固有的丰富性，这在传统方法中通常被忽视（例如，随着时间的推移逐渐演变的过程很难使用纯描述性方法捕获）。该项目的主要目的是为CANTAB测试开发计算模型，目前使用传统的描述性方法进行分析。剑桥认知拥有大量在普通人群中收集的数据（包括面对面和使用其在线平台），可用于此目的。在模型开发之后，将进行实验以了解模型参数如何受到心理健康问题（特别是抑郁症）症状以及药物干预（特别是认知增强药物）的影响。